A nondestructive safety diagnosis for lithium-ion battery modules was demonstrated with experimental data. The charging curve analysis (CCA) was selected for estimating the internal state of a lithium-ion battery cell and the cell operating conditions in a module. The safety threshold set by using CCA data was validated by thermal runaway tests for battery cells using an external heating method. The diagnosis for the module revealed not only its safety but also its discharge capacity (state of health (SOH)). An output image with comprehensive information including indicators to accumulate remaining battery performance values was successfully displayed.

In order to accurately predict the impact condition of each body region to the car surface from the initial impact condition between the car and powered two wheeler, the influence of the initial impact condition on the trajectory of the driver of the powered two wheeler was investigated by using FE collision simulations. Impact angle, and velocities of the both car and powered two wheeler were used as the parameter to define the initial impact condition. The trajectories of the Head, T1, Upper Thorax and Pelvis and velocities of these body parts were obtained from the time history of the coordinate data of the FE collision simulations. The trajectories of each body region showed following tendency: 1) The influence of impact angle, car velocity and powered two wheeler velocity on the trajectories became larger as the initial location of body parts was lower. Specifically, the Head moves approximately straight along with initial velocity direction until the Head impacts to the car. 2) Decrease of resultant velocity of each part of the body becomes small when the impact angle is different from perpendicular. The findings from this study can be used as the basic knowledge for the powered two wheeler driver’s trajectories to accurately predict the impact condition of each body region to the car surface from the initial impact condition between the car and powered two wheeler.

Traffic accidents on community roads, which frequently have intersections with poor visibility, are one of the social issues in Japan. Although safety technologies that utilize roadside sensors are expected to be effective for Japanese community roads, only a few roadside sensors and limited sensor coverage are available on community roads in practice. In such an environment, it is difficult to consistently track multiple traffic participants by associating sensor observations of them from one sensor coverage to another coverage. To address this difficulty, we propose a data association method for multi-target tracking on the assumption that targets can be outside the sensor coverage. The proposed method calculates the existence probability of each target being within the sensor coverage at each time step and incorporates it as a prior probability in the data association process. In the simulation experiments, comparisons with existing methods demonstrate that the proposed method achieves a higher association success rate in various conditions. Furthermore, real-world experiments validate the feasibility of the proposed method.

The quick and accurate prediction of occupant injuries in motor vehicle collisions helps emergency services respond more effectively and reduce casualties. Existing studies have mainly concentrated on predicting overall injury severity rather than examining injuries to specific body parts, which limits the precision of injury assessment and targeted emergency response. In this study, we developed a random forest-based model to predict injury severity in different body parts, including the head, face, neck, chest, abdomen, spine, and limbs. This enables emergency services to deliver precise and targeted responses after collisions. Furthermore, it facilitates a correlation analysis between various collision-contributing factors and body part-specific injuries.